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Amplifying influence through coordinated behaviour in social networks.

Derek Weber1,2, Frank Neumann1

  • 1School of Computer Science, University of Adelaide, Adelaide, SA Australia.

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PubMed
Summary
This summary is machine-generated.

This study introduces a novel temporal window approach to detect hidden networks of accounts engaging in coordinated online malicious behaviors like political misinformation and astroturfing, crucial for safeguarding democratic systems.

Keywords:
Coordinated amplificationCoordinated behaviourInformation campaignsOnline social networks

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Area of Science:

  • Social Network Analysis
  • Computational Social Science
  • Online Behavior Analysis

Background:

  • Online malicious behaviors, including political misinformation, astroturfing, and organized trolling, pose significant threats to democratic systems.
  • Existing research often focuses on identifying campaigns rather than the small, coordinated groups instigating them.
  • Detecting these hidden networks is crucial for understanding and mitigating their real-world impact.

Purpose of the Study:

  • To propose and validate a novel temporal window approach for revealing latent networks of cooperating accounts involved in online malicious behaviors.
  • To develop a method capable of identifying coordinated amplification strategies and other inauthentic behaviors using only account interactions and metadata.
  • To provide a framework suitable for near real-time application in detecting coordinated inauthentic behavior on social media.

Main Methods:

  • A pipeline approach extracting social media post elements, inferring account connections based on coordination strategies, and building a weighted network.
  • A novel community extraction method applied to the network to identify groups with high coordination evidence.
  • A temporal windowing mechanism, sliding frames, and a decay factor to address the temporal dynamics of online interactions.

Main Results:

  • The temporal window approach effectively detects coordinated groups and amplification strategies.
  • The method demonstrates utility in identifying malicious online behaviors in politically relevant datasets.
  • Validation against ground truth data using various analytical methods, including one-class classifiers, confirms the approach's effectiveness.

Conclusions:

  • The proposed temporal window approach offers a robust method for uncovering hidden networks behind online malicious behaviors.
  • This technique is valuable for understanding and combating coordinated inauthentic behavior that influences democratic processes.
  • The approach's adaptability for near real-time analysis enhances its practical application in cybersecurity and social media monitoring.